class_docstr: Calculates the values of a set of features for given instance ids.
code: def generate_default_df(self, instance_ids, extra_columns=None):
      default_row = []
      default_cols = []
      for f in self.feature_set.target_features:
          for name in f.get_feature_names():
              default_cols.append(name)
              default_row.append(f.default_value)
  
      default_matrix = [default_row] * len(instance_ids)
      default_df = pd.DataFrame(
          default_matrix,
          columns=default_cols,
          index=instance_ids,
          dtype="object",
      )
      index_name = self.entityset[
          self.feature_set.target_df_name
      ].ww.index
      default_df.index.name = index_name
      if extra_columns is not None:
          for c in extra_columns:
              if c not in default_df.columns:
                  default_df[c] = [np.nan] * len(instance_ids)
      return default_df
  
imports: from datetime import datetime
  from functools import partial
  import numpy
  import pandas
  import pandas.api.types
  from featuretools.entityset.relationship import RelationshipPath
  from featuretools.exceptions import UnknownFeature
  from featuretools.feature_base import AggregationFeature, DirectFeature, GroupByTransformFeature, IdentityFeature, TransformFeature
  from featuretools.utils import Trie
  from featuretools.utils.gen_utils import Library, get_relationship_column_id, import_or_none, is_instance
label_desc: Use image node_id 6 for calling the FeatureSetCalculator obj's underlying member method code with example usage: obj.generate_default_df(instance_ids, extra_columns) and returns: default_df